Machine learning techniques, and by extension cognitive learning techniques, involve extensive use of linear algebra and tensor mathematics. As such techniques lend themselves to parallel processing computational techniques, a variety of graphical processing units (GPUs) and other parallel computing capable hardware are targeted. Often this hardware is hosted in the cloud.
Since a proliferation of hardware platforms may be targeted, hosting a machine learning application presently involves custom coding to a chosen hardware platform. Typically a machine learning application will start as source code, which is subsequently compiled into object code and/or executable code specific to the chosen hardware platform.
However, compilation of source code targeting parallel platforms is difficult. Compilers might not be able to take advantage of parallelism on the hardware platform, and may generated essentially non-parallel code. Furthermore, some transformations during code generation may not be possible, or may be computationally intensive. Accordingly, preprocessing techniques, such as first pass compilation techniques, may lend themselves to compilation of source code containing linear algebra and tensor operations.